Penerapan Hukum Pidana Materil Terhadap Tindak Pidana Perjudian Online Jenis Togel Online Di Nagari Cupak Oleh Polres Solok Arosuka
Bibliographic record
Abstract
The progress of the times is followed by the increasingly sophisticated information technology, behind the convenience and convenience of internet services, deviant behaviors are born by utilizing advanced technology as a tool to achieve goals by committing crimes. The phenomenon of online gambling and its impact on individuals, especially players, tends to lead to the negative side. Online gambling is a type of online lottery that occurs in Jorong Pasar Baru Nagari Cupak, Gunung Talang District, Solok Regency. The perpetrator with the initials RN uses a cellphone as a means to play online lottery type gambling, the RN player plays online lottery type online gambling which is on the side of the road and close to residential areas. The research method used is empirical legal research, where this type of research is a legal research that examines the law whose concept is real behavior. Based on the results of the research that the author did that in the case of online gambling crimes at the Solok Arosuka Police, he applied Article 303 of the Criminal Code and Article 303 of the Criminal Code to the perpetrators and not based on Law Number 19 of 2016 concerning amendments to Law Number 11 of 2008 concerning Law No. -Information and Electronic Transaction Law.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.210 | 0.050 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".